Establishing Annotation Quality in Multi-label Annotations

Marian Marchal, Merel Scholman, Frances Yung, Vera Demberg

Abstract
In many linguistic fields requiring annotated data, multiple interpretations of a single item are possible. Multi-label annotations more accurately reflect this possibility. However, allowing for multi-label annotations also affects the chance that two coders agree with each other. Calculating inter-coder agreement for multi-label datasets is therefore not trivial. In the current contribution, we evaluate different metrics for calculating agreement on multi-label annotations: agreement on the intersection of annotated labels, an augmented version of Cohen’s Kappa, and precision, recall and F1. We propose a bootstrapping method to obtain chance agreement for each measure, which allows us to obtain an adjusted agreement coefficient that is more interpretable. We demonstrate how various measures affect estimates of agreement on simulated datasets and present a case study of discourse relation annotations. We also show how the proportion of double labels, and the entropy of the label distribution, influences the measures outlined above and how a bootstrapped adjusted agreement can make agreement measures more comparable across datasets in multi-label scenarios.
Anthology ID:
2022.coling-1.322
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3659–3668
Language:
URL:
https://aclanthology.org/2022.coling-1.322
DOI:
Bibkey:
Cite (ACL):
Marian Marchal, Merel Scholman, Frances Yung, and Vera Demberg. 2022. Establishing Annotation Quality in Multi-label Annotations. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3659–3668, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Establishing Annotation Quality in Multi-label Annotations (Marchal et al., COLING 2022)
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